Sentiment Mining within Social Media for Topic Identification

Social media has demonstrated itself to be a proven source of information towards the marketing of products. This unique source of data provides a rapid means of customer feedback that is used to support a number of business areas. Towards this purpose, we describe a methodology for the identification of topics associated with customer sentiment. This process first employs a Fisher Classification based approach towards sentiment analysis. By considering specific mutual information and word frequency distribution, topics are then identified within sentiment categories. The goal is to provide overall trends in sentiment along with associated subject matter (ie. why) as it supports a company's business. We demonstrate this methodology against data collected among a particular product line as obtained from Twitter advanced search.